• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.632-638
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• 2002

In novelty detection, one attempts to discriminate abnormal patterns from normal ones. Novelty detection is quite difficult since, unlike usual two class classification problems, only normal patterns are available for training. Auto-Associative Multi-Layer Perceptron (AAMLP) has been shown to provide a good performance based upon the property that novel patterns usually have larger auto-associative errors. In this paper, we give a mathematical analysis of 2-layer AAMLP's output characteristics and empirical results of 2-layer and 4-layer AAMLPs. Various activation functions such as linear, saturated linear and sigmoid are compared. The 2-layer AAMLPs cannot identify non-linear boundaries while the 4-layer ones can. When the data distribution is multi-modal, then an ensemble of AAMLPs, each of which is trained with pre-clustered data is required. This paper contributes to understanding of AAMLP networks and leads to practical recommendations regarding its use.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 추계학술대회 논문집
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• pp.409-412
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• 2000

A high performance EEG signal measurement system is fabricated. It consists of high precision pre-amplifier and auto identification bandwidth unit. High precision pre-amplifier is composed of signal generator, signal amplifier with a impedance converter, body driver and isolation amplifier. The pre-amplifier is designed for low noise characteristics, high CMRR, high input impedance, high IMRR and safety, Auto identification bandwidth unit is composed of AD-converter and PIC micro-controller for real time processing EEG signal. The performance of EEG signal measurement system has been shown the classified bandwidth through the clinical demonstrations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• 한국도서관정보학회지
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• 제19권
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• pp.35-57
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• 1992

This study is an attempt to provide some helpful data for the design and the implementation of the expert system for the book-classification based on the analysis of various cases of the classification-expert system models. Following the introduction, the concepts and some features of an expert system were overviewed in the second chapter, on the basis of which the following concrete cases were introduced and analyzed in the third chapter : (1) ACN System for NC, (2) Expert System for NDC, (3) Expert System for UDC, (4) Herba Medica System, (5) Expert System for IPC, (6) Stratcyclode Project, (7) Expert System for Classification of INIS Database, (8) AutoBC System, and etc. In the conclusion, for the development of the classification-expert system, it was turned out that constructing a new system by using an AI language such as Prolog or LISP is more desirable than employing any one of expert system shells. Together it is necessary for the following requirements to be met : (1) The subject concept of a document elicited should be accurate. (2) Not only a domain knowledge but also the knowledge covering all the subjects should be represented in the knowledge-bases. (3) The knowledge-bases should be organized in such a way that the characteristics of the knowledge about classification should be well defined. (4) rule-base consisting of accurate rules about classification should be made. (5) It should be possible for classification code wanted to be generated immediately.

• International Journal of Automotive Technology
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• 제7권2호
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• pp.195-199
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• 2006

According to the United States FMVSS 208, every passenger car on the market after September of 2006 must install a safety system, which can deploy the airbag with different intensity or suppression based on the passenger type, to reduce infant and child injuries from airbag deployments. The Weight Classification System, which has been developed by Hyundai Autonet, is a system that classifies the person occupying the passenger seat. To overcome sensing problems due to the weight sensors small voltage, the Distributed Systems Interface is adopted.

• 인터넷정보학회논문지
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• 제20권1호
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• pp.57-65
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• 2019

200 만명 이상의 회원을 보유하고 있는 "도매꾹" B2B 온라인 쇼핑몰인 경우70% 이상의 시장 점유율로 하루에 80만개 이상의 아이템이 판매되고 있다. 하지만, 동일하거나 유사한 물품이 서로 다른 카탈로그 엔트리에 저장 및 등록되어 있기 때문에 구매자가 아이템을 검색하는 과정에서 어려움을 느끼며 B2B 대형 쇼핑몰 관리에도 문제점이 발생하고 있다. 따라서 이에 대한 해결 방안으로 본 연구에서는 대단위 쇼핑몰 구매 정보를 기반으로 지도-학습 머신러닝 기법을 적용하여 상품에 대한 카탈로그 목록 자동 분류 및 추천 시스템을 개발하였다. 구체적으로 판매자가 자연어 형태로 물품 등록 정보를 입력하면 KoNLPy 형태소 분석 과정을 수행하였으며, Naïve Bayes 분류 방식을 응용하여 물품에 가장 적합한 카탈로그 정보를 자동으로 추천해주는 시스템을 구현하였다. 정확도가 향상된 카테고리 목록을 구축하여 결과적으로 검색 속도와 쇼핑몰 매출을 향상시키는 효과가 있었다.

• 한국항행학회논문지
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• 제26권6호
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• pp.528-535
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• 2022

항공기 제작 및 운송 기술 발달로 국내 항공산업은 비약적인 발전을 이루었으나, 항공안전 사고는 지속해서 발생하고 있다. 관리 감독기관에서는 위험기반 항공안전데이터를 기반으로 위해 요인과 위험도를 분류하고, 운송사업자별 안전 경향성 파악과 취약분야를 도출하여 사전점검을 수행함으로써 사건·사고를 사전 예방중에 있다. 그러나 자연어 형식으로 기술된 항공안전데이터의 휴먼 분류는 지식과 경험, 성향에 따라 서로 다른 분류 결과를 초래하고, 이벤트 내용의 의미 파악 및 분류를 위한 작업에 상당한 시간을 소요케 한다. 이에, 본 논문에서는 KoBERT 모델을 fine-tunning하고 5천 건 이상의 항공안전데이터를 기계학습 시켜 신규 데이터의 분류 값을 예측한 결과 79.2%의 정확성을 보였다. 그리고 유사 이벤트에 대해 동일한 결과 예측과 fail 된 데이터 중 일부는 휴먼 에러에 의한 오류임을 확인할 수 있었다.

• 인터넷정보학회논문지
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• 제22권3호
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.